Image Semantic Understanding via Vector Space and Huffman Coding
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Solution Overview
Problem
Current image recognition techniques using deep learning struggle to provide accurate and specific semantic context due to reliance on pixel extrapolation, leading to loss of semantic meaning and inaccurate predictions, especially with low-resolution images.
Innovation Solution
A computer-implemented method utilizing a convolutional neural network to generate a vector space of object features, followed by Huffman coding to identify broad to specific vocabulary, enabling deep semantic context learning and understanding by extracting hierarchical features and concatenating them into a fixed length feature vector space for accurate image description.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel extrapolation is used in current image recognition techniques, then processing speed is maintained, but semantic meaning is lost and prediction accuracy deteriorates
Solution Approach 1:
The patent replaces the mechanical pixel extrapolation process with a vector-based semantic representation system. Instead of mechanically interpolating pixel values to upscale images, the system extracts semantic features, converts them to vectors, and reconstructs images through vector arithmetic operations. This substitution preserves semantic meaning while achieving high-resolution reconstruction, directly resolving the contradiction between maintaining processing speed and preserving semantic information.
Solution Approach 2:
The patent introduces vector representations as an intermediary between the original image and the reconstructed high-resolution image. The vector space serves as a mediator that captures semantic meaning during the transformation process. By operating in this intermediate vector space rather than directly manipulating pixels, the system preserves semantic information while enabling accurate image reconstruction, thus resolving the information loss problem.
2Measurement precision
If deep learning architectures use multiple layers of processing nodes, then feature extraction capability is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential semantic features from images using convolutional neural networks, converting them into compact vector representations. This extraction process removes redundant information and focuses on the most meaningful characteristics, achieving high feature extraction accuracy while significantly reducing computational complexity compared to processing entire high-resolution images through multiple deep learning layers.
Solution Approach 2:
The patent transforms images from pixel-space to vector-space, changing the fundamental parameters of representation. By operating with vectors instead of pixel grids, the system achieves equivalent or superior feature extraction capability with dramatically reduced computational complexity, as vector operations are inherently more efficient than convolutional operations on high-resolution images.
3Measurement precision
If vector space arithmetic and Huffman encoding are used, then semantic context precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary encoding of semantic features into Huffman-coded vector representations during the training phase. This preliminary action creates a compressed semantic dictionary that can be quickly referenced during inference. By pre-processing and compressing the semantic vocabulary, the system achieves high semantic context accuracy during runtime without incurring the computational overhead of complex encoding operations, thus resolving the time-precision tradeoff.
Data Source
AI summary
Identifying words to accurately describe, with a range of specificity, an image is provided. A vector space corresponding to the image is generated using a convolutional neural network to extract a hierarchy of features ranging from broad to specific from the image. Closest vocabulary ranging from broad to specific are identified for the image using Huffman coding on the vector space. Accurate words ranging from broad to specific are identified that describe the image based on vocabulary output of the Huffman coding on the vector space. The accurate words ranging from broad to specific describing the image are output.